Current solutions monitor driving behavior by evaluating vehicle dynamics. Driving risk is correlated with speed, braking, cornering, without taking into account traffic, weather conditions, attention paid by the driver to events happening on the road, ability to control the vehicle in unexpected situations, proper physical and mental conditions.
Car accidents in the US (400 k+ in 2015) are on the rise (+9%), after a decade of slow but steady declines. Although safer cars and improved driving assist equipment help prevent accidents, distracted driving is more than offsetting all these benefits. State bans on the use of cell phones in cars seem not to work. Mobile apps that intercept distracting calls or the use of apps are easy to circumvent and distractions can also come from sources other than phones.
The old fashioned way to solve the problem is to rely on the collaboration of a front seat passenger who 1) is aware of sudden driving risks, 2) can tell whether the driver is paying sufficient attention to driving, and 3) will warn the driver when needed. But a front seat passenger is not always there to help out.
Over the recent years, basic telematics services have been introduced to encourage safe driving via Usage Based Insurance (UBI) plans.
The present invention represents an evolution of UBI telematics systems by combining analytics of telematics data, driver observed behavior and performance, to compute driving risk scores. Most importantly, the invention provides real time and personalized feedback to the driver to prevent dangerous situations caused by distraction in the first place.
The present invention relates to a method to evaluate
a) attentiveness of people while performing a task or communicating with another party (person or machine
b) estimated level of risk associated with the surrounding environment
c) the margin between the level of available attention and the attention level required by the task or communication
in order to provide
d) useful feedback to the person being observed on their behavior
e) suggestions on how to change behavior in order to reduce or minimize the risk.
Artificial Intelligence (AI) is used to convert observed patterns into behavior profiles, to refine them over multiple observations and to create group statistics across similar situations.
As an example, one important application includes driving risk profiling and prevention of accidents caused by distracted and drowsy driving, using machine vision and AI to create the ultimate Digital Assistant with Copilot Expertise. All drivers can benefit, particularly teenage drivers, elderly drivers or drivers with chronic conditions.
Fleet management companies, car insurance companies, ride sharing and rental car companies as well as healthcare providers can take advantage of this invention to improve, fine tune, personalize their services and make them more cost competitive.
The present invention provides a platform for Driver Attention Management (Smart Driver Monitoring) to address escalating problems of unsafe driving. It covers end to end the attention spectrum, ranging from distracted driving to experiencing drowsiness on long, boring stretches of road.
Mobile devices and their apps are not designed to be distraction-free. They ignore the stress level that the driver may be under, possibly requiring full attention at a split second notice. At the same time, drivers who benefit from reduced cognitive load ensured by sophisticated driver assist are more easily subject to drowsiness, another leading cause of fatal accidents.
The vast majority of driving assist solutions ignore drivers' fatigue, stress, wellness, fitness, reaction capabilities to anticipate risks and adjust warning margins.
With current logging driver information, known monitoring solutions do not support secure data access rights with high degrees of configuration flexibility provide real time feedback mechanisms, provide ability to suspend/revoke select access rights at any time, including while driving. With the present invention Electronic Driving Record (EDR) the driver can reinstate access rights dynamically while driving or at the end of a trip, let the driver identify who gets to see what EDR data, when and how create accurate driving behavior models support inclusion of sensors data (e.g. health-related) measure and log attention level of the driver. Current systems further fail to address the privacy implications with UBI data gathering.